Why would you combine dense and sparse vector search instead of relying on dense embeddings alone?

I would combine them because dense embeddings and sparse retrieval fail in different ways. Dense embeddings are excellent at semantic similarity and paraphrases, but they can weaken exact matching for names, SKUs, error codes, abbreviations, model numbers, or rare domain terminology. Sparse retrieval preserves strong lexical evidence for those cases. Running both and fusing their candidate lists gives the system a broader recall envelope: dense handles meaning while sparse handles exact or highly discriminative terms. The trade-off is extra indexing, storage, and query work. A common mistake is assuming the dense model has learned every identifier exactly; in production search I want empirical evidence before removing lexical retrieval.